def _detect(self,im):
'''
void cvCornerHarris( const CvArr* image, CvArr* harris_responce, int block_size, int aperture_size=3, double k=0.04 );
'''
gray = im.asOpenCVBW()
#gray = opencv.cvCreateImage( opencv.cvGetSize(cvim), 8, 1 );
corners = cv.CreateImage( cv.GetSize(gray), 32, 1 );
#opencv.cvCvtColor( cvim, gray, opencv.CV_BGR2GRAY );
cv.CornerHarris(gray,corners,self.block_size,self.aperture_size,self.k)
data_buffer = corners.tostring()
corners = np.frombuffer(data_buffer,np.float32).reshape(corners.height,corners.width).transpose()
footprint = np.ones((3,3))
mx = ndi.maximum_filter(corners, footprint = footprint)
local_maxima = (corners == mx) * (corners != np.zeros(corners.shape)) # make sure to remove completly dark points
points = np.nonzero(local_maxima)
del local_maxima
points = np.array([points[0],points[1]]).transpose()
L = []
for each in points:
L.append((corners[each[0],each[1]],each[0],each[1],None))
return L
def corner_detection(image):
img_or = cv.LoadImage(image)
img = cv.LoadImage(image, cv.CV_LOAD_IMAGE_GRAYSCALE)
w = img.width
h = img.height
print w, h
cornerMap = cv.CreateMat(h, w, cv.CV_32FC1)
print cornerMap
#Harris Corner Detection
cv.CornerHarris(img, cornerMap, 3)
corners = []
#Recorriendo toda la imagen
for x in range(0, h):
for y in range(0, w):
harris = cv.Get2D(cornerMap, x, y)
#Validar la respuesta de deteccion de esquinas
if harris[0] > 10e-06:
#Dibujar un circulo en la imagen original
cv.Circle(img_or, (y, x), 2, cv.RGB(155, 0, 25))
corners.append([y, x])
#Muestra la imagen y la guarda
cv.ShowImage('Harris', img_or)
cv.SaveImage('harris.jpg', img_or)
cv.WaitKey()
return corners, img
def processFrames(self):
self.vidcap = cv2.VideoCapture(self.path)
count = 0
success, image = self.vidcap.read()
print success
self.createWindows()
while True:
success, image = self.vidcap.read()
if not success:
return
spare = cv.fromarray(image)
size = (spare.width / 2, spare.height / 2)
cv.Smooth(spare, spare, cv.CV_GAUSSIAN, BLUR_SIZE, BLUR_SIZE)
out = cv.CreateImage(size, 8, 3)
cv.PyrDown(spare, out)
yuv = cv.CreateImage(size, 8, 3)
gray = cv.CreateImage(size, 8, 1)
canny = cv.CreateImage(size, 8, 1)
sobel = cv.CreateImage(size, 8, 1)
harris = cv.CreateImage(size, cv.IPL_DEPTH_32F, 1)
cv.CvtColor(out, yuv, cv.CV_BGR2YCrCb)
cv.Split(yuv, gray, None, None, None)
cv.Canny(gray, canny, 50, 200, 3)
cv.CornerHarris(gray, harris, 3)
cv.Sobel(gray, sobel, 1, 0, 3)
cv.ConvertScale(canny, canny, -1, 255)
cv.ConvertScale(sobel, sobel, -1, 255)
for y in range(0, out.height):
for x in range(0, out.width):
harr = cv.Get2D(sobel, y, x)
if harr[0] < 10e-06:
cv.Circle(out, (x, y), 2, cv.RGB(155, 0, 25))
#cv2.imwrite("frame%d.jpg" % count, np.asarray(canny[:,:]))
cv.ShowImage('canny', canny)
#cv.ShowImage( 'harris' , harris )
cv.ShowImage('sobel', sobel)
cv.ShowImage('output', out)
if cv2.waitKey(1) == 27:
break
count += 1
return
def getCornerMap(frame):
global corner
cornerMap = cv.CreateMat(frame.height, frame.width, cv.CV_32FC1)
if (corner < 1):
corner = 1
cv.SetTrackbarPos("corner", "RGB module", 1)
cv.CornerHarris(frame, cornerMap, corner)
return (cornerMap)
def detect_harris(array):
image = as_ipl(array)
cornerMap = cv.CreateMat(image.height, image.width, cv.CV_32FC1)
cv.CornerHarris(image, cornerMap, 3)
signature = np.zeros((image.height, image.width)).astype('uint8')
for y in range(0, image.height):
for x in range(0, image.width):
harris = cv.Get2D(cornerMap, y, x)
if harris[0] > 10e-06:
cv.Circle(image,(x,y),1,cv.RGB(155, 0, 25), 3,8,0)
signature[y, x] = 255
corner_array = np.asarray(signature[:, :])
return corner_array
imshow("Red channel", channelR)
imshow("Green channel", channelG)
imshow("Blue channel", channelB)
# Show image in black and white and in colour.
cv.ShowImage("Normal feed", imcolor)
cv.ShowImage("Black and white", image)
# Initialise the octagon
octagon(imcolor)
# *** GP: Destination of the harris algorithm
cornerMap = cv.CreateMat(image.height, image.width, cv.CV_32FC1)
# *** GP: OpenCV corner detection
cv.CornerHarris(image, cornerMap, 3)
# *** GP: Iterate through each pixel to get the final command.
for y in range(0, image.height):
for x in range(0, image.width):
harris = cv.Get2D(cornerMap, y, x) # get the x,y value
# *** GP: check the corner detector response
if harris[0] > 10e-06:
# *** GP: draw a small circle on the original image to represent the corner
# *** USER: change the colour of the detected corner.
cv.Circle(imcolor, (x, y), 2, cv.RGB(155, 0, 25))
# Check if it is in one of the 8 points.
active(x, y)
def harris():
import cv
import math
global a
imcolor = cv.LoadImage('leaf1.jpg')
image = cv.LoadImage('leaf1.jpg', cv.CV_LOAD_IMAGE_GRAYSCALE)
cornerMap = cv.CreateMat(image.height, image.width, cv.CV_32FC1)
# OpenCV corner detection
cv.CornerHarris(image, cornerMap, 3)
pts = []
print "one"
for y in range(0, image.height):
for x in range(0, image.width):
harris = cv.Get2D(cornerMap, y, x) # get the x,y value
# check the corner detector response
if harris[0] > 10e-06:
## print x
## print y
# draw a small circle on the original image
cv.Circle(imcolor, (x, y), 2, cv.RGB(155, 0, 25))
pts.append((x, y))
ang = []
for i in range(1, len(pts) - 2):
x1 = float(pts[i - 1][0])
y1 = float(pts[i - 1][1])
x2 = float(pts[i][0])
y2 = float(pts[i][1])
try:
s1 = (y2 - y1) / (x2 - x1)
except:
pass
x1 = float(pts[i][0])
y1 = float(pts[i][1])
x2 = float(pts[i + 1][0])
y2 = float(pts[i + 1][1])
try:
s2 = (y2 - y1) / (x2 - x1)
except:
pass
ang.append(abs(math.atan(s2 - s1)))
imcolor = cv.LoadImage(a)
image = cv.LoadImage(a, cv.CV_LOAD_IMAGE_GRAYSCALE)
cornerMap = cv.CreateMat(image.height, image.width, cv.CV_32FC1)
# OpenCV corner detection
cv.CornerHarris(image, cornerMap, 3)
pts = []
print "one"
for y in range(0, image.height):
for x in range(0, image.width):
harris = cv.Get2D(cornerMap, y, x) # get the x,y value
# check the corner detector response
if harris[0] > 10e-06:
## print x
## print y
# draw a small circle on the original image
cv.Circle(imcolor, (x, y), 2, cv.RGB(155, 0, 25))
pts.append((x, y))
ang1 = []
for i in range(1, len(pts) - 2):
x1 = float(pts[i - 1][0])
y1 = float(pts[i - 1][1])
x2 = float(pts[i][0])
y2 = float(pts[i][1])
try:
s1 = (y2 - y1) / (x2 - x1)
except:
pass
x1 = float(pts[i][0])
y1 = float(pts[i][1])
x2 = float(pts[i + 1][0])
y2 = float(pts[i + 1][1])
try:
s2 = (y2 - y1) / (x2 - x1)
except:
pass
ang1.append(abs(math.atan(s2 - s1)))
ind = min(len(ang), len(ang1))
print ind
val = 0
for i in range(0, ind):
val += ang[i] - ang1[i]
print val
if val <= 0:
print 'images are similar...'
exit()
else:
print "Different Images"
exit()
